COVID-19 diagnosis with artificial intelligence algorithms

When the covid-19 virus initially spread globally in 2019, As a result, numerous people passed away. Due to this sickness, medical staff members are busier than ever. For reducing this load, computer-aided diagnostic tools and machine learning will assist in some ways. Any scientific investigation into this disease can help it be eliminated rapidly. In our study, we utilized the Covid-19 dataset which is publicly accessible (14486 cases) from the Kaggle website. Training and test data categories were created from the dataset, using 80% of the data for training and 20% for testing. This research has two main stages: First, we extract Visual Geometry Group (VGG16) features from the dataset, then based on these extracted features, five machine learning algorithms were employed for classification (k-nearest neighbor (KNN), random forest (RF), extreme gradient boosting (XGBoost), decision trees (DT), and support vector machine (SVM)). Second, we extract gray-level co-occurrence matrix (GLCM) features from the dataset. Then, we applied the same five machine learning algorithms (DT, RF, KNN, XGBoost, and SVM) for classification based on these extracted features. Performance metrics were examined utilizing a confusion matrix, accuracy, recall, and precision together with the F1 score.Regarding classification accuracy, the Support vector machine algorithm and VGG16 were the classification techniques that excel at 98.22% accuracy and achieve the impressively rapid and best result.